Texture Classification for Liver Tissues from Ultrasonic B-Scan Images Using Testified PNN

Yan SUN  Jianming LU  Takashi YAHAGI  

IEICE TRANSACTIONS on Information and Systems   Vol.E89-D    No.8    pp.2420-2428
Publication Date: 2006/08/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.8.2420
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
fractal dimension,  multifractal,  DBC (Differential Boxing-Counting),  FDWT,  PNN (Pyramid Neural Network),  cross-validation,  

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Visual criteria for diagnosing liver diseases, such as cirrhosis, from ultrasound images can be assisted by computerized texture classification. This paper proposes a system applying a PNN (Pyramid Neural Network) for classifying the hepatic parenchymal diseases in ultrasonic B-scan texture. In this study, we propose a multifractal-dimensions method to select the patterns for the training set and the validation sets. A modified box-counting algorithm is used to calculate the dimensions of the B-scan images. FDWT (Fast Discrete Wavelet Transform) is applied for feature extraction during the preprocessing. The structure of the proposed neural network is testified by training and validation sets by cross-validation method. The performance of the proposed system and a system based on the conventional multilayer network architecture is compared. The results show that, compared with the conventional 3-layer neural network, the performance of the proposed pyramid neural network is improved by efficiently utilizing the lower layer of the neural network.

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